The neuroanatomy of visual extinction following right hemisphere brain damage: Insights from multivariate and Bayesian lesion analyses in acute stroke.

Multi-target attention, that is, the ability to attend and respond to multiple visual targets presented simultaneously on the horizontal meridian across both visual fields, is essential for everyday real-world behaviour. Given the close link between the neuropsychological deficit of extinction and attentional limits in healthy subjects, investigating the anatomy that underlies extinction is uniquely capable of providing important insights concerning the anatomy critical for normal multi-target attention. Previous studies into the brain areas critical for multi-target attention and its failure in extinction patients have, however, produced heterogeneous results. In the current study, we used multivariate and Bayesian lesion analysis approaches to investigate the anatomical substrate of visual extinction in a large sample of 108 acute right hemisphere stroke patients. The use of acute stroke patient data and multivariate/Bayesian lesion analysis approaches allowed us to address limitations associated with previous studies and so obtain a more complete picture of the functional network associated with visual extinction. Our results demonstrate that the right temporo-parietal junction (TPJ) is critically associated with visual extinction. The Bayesian lesion analysis additionally implicated the right intraparietal sulcus (IPS), in line with the results of studies in neurologically healthy participants that highlighted the IPS as the area critical for multi-target attention. Our findings resolve the seemingly conflicting previous findings, and emphasise the urgent need for further research to clarify the precise cognitive role of the right TPJ in multi-target attention and its failure in extinction patients.

Disconnection in a left-hemispheric temporo-parietal network impairs multiplication fact retrieval.

Arithmetic fact retrieval has been suggested to recruit a left-lateralized network comprising perisylvian language areas, parietal areas such as the angular gyrus (AG), and non-neocortical structures such as the hippocampus. However, the underlying white matter connectivity of these areas has not been evaluated systematically so far. Using simple multiplication problems, we evaluated how disconnections in parietal brain areas affected arithmetic fact retrieval following stroke. We derived disconnectivity measures by jointly considering data from n = 73 patients with acute unilateral lesions in either hemisphere and a white-matter tractography atlas (HCP-842) using the Lesion Quantification Toolbox (LQT). Whole-brain voxel-based analysis indicated a left-hemispheric cluster of white matter fibers connecting the AG and superior temporal areas to be associated with a fact retrieval deficit. Subsequent analyses of direct gray-to-gray matter disconnections revealed that disconnections of additional left-hemispheric areas (e.g., between the superior temporal gyrus and parietal areas) were significantly associated with the observed fact retrieval deficit. Results imply that disconnections of parietal areas (i.e., the AG) with language-related areas (i.e., superior and middle temporal gyri) seem specifically detrimental to arithmetic fact retrieval. This suggests that arithmetic fact retrieval recruits a widespread left-hemispheric network and emphasizes the relevance of white matter connectivity for number processing.

Bayesian lesion-deficit inference with Bayes factor mapping: Key advantages, limitations, and a toolbox.

Statistical lesion-symptom mapping is largely dominated by frequentist approaches with null hypothesis significance testing. They are popular for mapping functional brain anatomy but are accompanied by some challenges and limitations. The typical analysis design and the structure of clinical lesion data are linked to the multiple comparison problem, an association problem, limitations to statistical power, and a lack of insights into evidence for the null hypothesis. Bayesian lesion deficit inference (BLDI) could be an improvement as it collects evidence for the null hypothesis, i.e. the absence of effects, and does not accumulate α-errors with repeated testing. We implemented BLDI by Bayes factor mapping with Bayesian t-tests and general linear models and evaluated its performance in comparison to frequentist lesion-symptom mapping with a permutation-based family-wise error correction. We mapped the voxel-wise neural correlates of simulated deficits in an in-silico-study with 300 stroke patients, and the voxel-wise and disconnection-wise neural correlates of phonemic verbal fluency and constructive ability in 137 stroke patients. Both the performance of frequentist and Bayesian lesion-deficit inference varied largely across analyses. In general, BLDI could find areas with evidence for the null hypothesis and was statistically more liberal in providing evidence for the alternative hypothesis, i.e. the identification of lesion-deficit associations. BLDI performed better in situations in which the frequentist method is typically strongly limited, for example with on average small lesions and in situations with low power, where BLDI also provided unprecedented transparency in terms of the informative value of the data. On the other hand, BLDI suffered more from the association problem, which led to a pronounced overshoot of lesion-deficit associations in analyses with high statistical power. We further implemented a new approach to lesion size control, adaptive lesion size control, that, in many situations, was able to counter the limitations imposed by the association problem, and increased true evidence both for the null and the alternative hypothesis. In summary, our results suggest that BLDI is a valuable addition to the method portfolio of lesion-deficit inference with some specific and exclusive advantages: it deals better with smaller lesions and low statistical power (i.e. small samples and effect sizes) and identifies regions with absent lesion-deficit associations. However, it is not superior to established frequentist approaches in all respects and therefore not to be seen as a general replacement. To make Bayesian lesion-deficit inference widely accessible, we published an R toolkit for the analysis of voxel-wise and disconnection-wise data.

Strategies for feature extraction from structural brain imaging in lesion-deficit modelling.

High-dimensional modelling of post-stroke deficits from structural brain imaging is highly relevant to basic cognitive neuroscience and bears the potential to be translationally used to guide individual rehabilitation measures. One strategy to optimise model performance is well-informed feature selection and representation. However, different feature representation strategies were so far used, and it is not known what strategy is best for modelling purposes. The present study compared the three common main strategies: voxel-wise representation, lesion-anatomical componential feature reduction and region-wise atlas-based feature representation. We used multivariate, machine-learning-based lesion-deficit models to predict post-stroke deficits based on structural lesion data. Support vector regression was tuned by nested cross-validation techniques and tested on held-out validation data to estimate model performance. While we consistently found the numerically best models for lower-dimensional, featurised data and almost always for principal components extracted from lesion maps, our results indicate only minor, non-significant differences between different feature representation styles. Hence, our findings demonstrate the general suitability of all three commonly applied feature representations in lesion-deficit modelling. Likewise, model performance between qualitatively different popular brain atlases was not significantly different. Our findings also highlight potential minor benefits in individual fine-tuning of feature representations and the challenge posed by the high, multifaceted complexity of lesion data, where lesion-anatomical and functional criteria might suggest opposing solutions to feature reduction.

An empirical comparison of univariate versus multivariate methods for the analysis of brain-behavior mapping.

Lesion symptom mapping (LSM) tools are used on brain injury data to identify the neural structures critical for a given behavior or symptom. Univariate lesion symptom mapping (ULSM) methods provide statistical comparisons of behavioral test scores in patients with and without a lesion on a voxel by voxel basis. More recently, multivariate lesion symptom mapping (MLSM) methods have been developed that consider the effects of all lesioned voxels in one model simultaneously. In the current study, we provide a much-needed systematic comparison of several ULSM and MLSM methods, using both synthetic and real data to identify the potential strengths and weaknesses of both approaches. We tested the spatial precision of each LSM method for both single and dual (network type) anatomical target simulations across anatomical target location, sample size, noise level, and lesion smoothing. Additionally, we performed false positive simulations to identify the characteristics associated with each method's spurious findings. Simulations showed no clear superiority of either ULSM or MLSM methods overall, but rather highlighted specific advantages of different methods. No single method produced a thresholded LSM map that exclusively delineated brain regions associated with the target behavior. Thus, different LSM methods are indicated, depending on the particular study design, specific hypotheses, and sample size. Overall, we recommend the use of both ULSM and MLSM methods in tandem to enhance confidence in the results: Brain foci identified as significant across both types of methods are unlikely to be spurious and can be confidently reported as robust results.

Mapping of long-term cognitive and motor deficits in pediatric cerebellar brain tumor survivors into a cerebellar white matter atlas.

PURPOSE: Diaschisis of cerebrocerebellar loops contributes to cognitive and motor deficits in pediatric cerebellar brain tumor survivors. We used a cerebellar white matter atlas and hypothesized that lesion symptom mapping may reveal the critical lesions of cerebellar tracts. METHODS: We examined 31 long-term survivors of pediatric posterior fossa tumors (13 pilocytic astrocytoma, 18 medulloblastoma). Patients underwent neuronal imaging, examination for ataxia, fine motor and cognitive function, planning abilities, and executive function. Individual consolidated cerebellar lesions were drawn manually onto patients' individual MRI and normalized into Montreal Neurologic Institute (MNI) space for further analysis with voxel-based lesion symptom mapping. RESULTS: Lesion symptom mapping linked deficits of motor function to the superior cerebellar peduncle (SCP), deep cerebellar nuclei (interposed nucleus (IN), fastigial nucleus (FN), ventromedial dentate nucleus (DN)), and inferior vermis (VIIIa, VIIIb, IX, X). Statistical maps of deficits of intelligence and executive function mapped with minor variations to the same cerebellar structures. CONCLUSION: We identified lesions to the SCP next to deep cerebellar nuclei as critical for limiting both motor and cognitive function in pediatric cerebellar tumor survivors. Future strategies safeguarding motor and cognitive function will have to identify patients preoperatively at risk for damage to these critical structures and adapt multimodal therapeutic options accordingly.

Hippocampal diaschisis contributes to anosognosia for hemiplegia: Evidence from lesion network-symptom-mapping.

Anosognosia for hemiplegia (AHP) is known to be associated with lesions to the motor system combined with varying lesions to the right insula, premotor cortex, parietal lobe or hippocampus. Due to this widespread cortical lesion distribution, AHP can be understood best as a network disorder. We used lesion maps and behavioral data (n â€‹= â€‹49) from two previous studies on AHP and performed a lesion network-symptom-mapping (LNSM) analysis. This new approach permits the identification of relationships between behavior and regions connected to the lesion site based on normative functional connectome data. In a first step, using ordinary voxel-based lesion-symptom mapping, we found an association of AHP with lesions in the right posterior insula. This is in accordance with previous studies. Applying LNSM, we were able to additionally identify a region in the right posterior hippocampus where AHP was associated with significantly higher normative lesion connectivity. Notably, this region was spared by infarction in all patients. We therefore argue that remote neuronal dysfunction caused by disrupted functional connections between the lesion site and the hippocampus (i.e. diaschisis) contributed to the phenotype of AHP. An indirect affection of the hippocampus may lead to memory deficits which, in turn, impair the stable encoding of updated beliefs on the bodily state thus contributing to the multifactorial phenomenon of AHP.

Post-stroke cognitive deficits rarely come alone: Handling co-morbidity in lesion-behaviour mapping.

Post-stroke behavioural symptoms often correlate and systematically co-occur with each other, either because they share cognitive processes, or because their neural correlates are often damaged together. Thus, neuropsychological symptoms often share variance. Many previous lesion-behaviour mapping studies aimed to methodologically consider this shared variance between neuropsychological variables. A first group of studies controlled the behavioural target variable for the variance explained by one or multiple other variables to obtain a more precise mapping of the target variable. A second group of studies focused on the shared variance of multiple variables itself with the aim to map neural correlates of cognitive processes that are shared between the original variables. In the present study, we tested the validity of these methods by using real lesion data and both real and simulated data sets. We show that the variance that is shared between post-stroke behavioural variables is ambiguous, and that mapping procedures that consider this variance are prone to biases and artefacts. We discuss under which conditions such procedures could still be used and what alternative approaches exist.

Using machine learning-based lesion behavior mapping to identify anatomical networks of cognitive dysfunction: Spatial neglect and attention.

Previous lesion behavior studies primarily used univariate lesion behavior mapping techniques to map the anatomical basis of spatial neglect after right brain damage. These studies led to inconsistent results and lively controversies. Given these inconsistencies, the idea of a wide-spread network that might underlie spatial orientation and neglect has been pushed forward. In such case, univariate lesion behavior mapping methods might have been inherently limited in detecting the presumed network due to limited statistical power. By comparing various univariate analyses with multivariate lesion-mapping based on support vector regression, we aimed to validate the network hypothesis directly in a large sample of 203 newly recruited right brain damaged patients. If the exact same correction factors and parameter combinations (FDR correction and dTLVC for lesion size control) were used, both univariate as well as multivariate approaches uncovered the same complex network pattern underlying spatial neglect. At the cortical level, lesion location dominantly affected the temporal cortex and its borders into inferior parietal and occipital cortices. Beyond, frontal and subcortical gray matter regions as well as white matter tracts connecting these regions were affected. Our findings underline the importance of a right network in spatial exploration and attention and specifically in the emergence of the core symptoms of spatial neglect.

Reprint of: Mapping human brain lesions and their functional consequences.

Neuroscience has a long history of inferring brain function by examining the relationship between brain injury and subsequent behavioral impairments. The primary advantage of this method over correlative methods is that it can tell us if a certain brain region is necessary for a given cognitive function. In addition, lesion-based analyses provide unique insights into clinical deficits. In the last decade, statistical voxel-based lesion behavior mapping (VLBM) emerged as a powerful method for understanding the architecture of the human brain. This review illustrates how VLBM improves our knowledge of functional brain architecture, as well as how it is inherently limited by its mass-univariate approach. A wide array of recently developed methods appear to supplement traditional VLBM. This paper provides an overview of these new methods, including the use of specialized imaging modalities, the combination of structural imaging with normative connectome data, as well as multivariate analyses of structural imaging data. We see these new methods as complementing rather than replacing traditional VLBM, providing synergistic tools to answer related questions. Finally, we discuss the potential for these methods to become established in cognitive neuroscience and in clinical applications.

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioural approaches in cognitive neuroscience. Several studies applied and validated support vector regression-based lesion symptom mapping (SVR-LSM) to map anatomo-behavioural relations. However, this promising method, as well as the multivariate approach per se, still bears many open questions. By using large lesion samples in three simulation experiments, the present study empirically tested the validity of several methodological aspects. We found that (i) correction for multiple comparisons is required in the current implementation of SVR-LSM, (ii) that sample sizes of at least 100-120 subjects are required to optimally model voxel-wise lesion location in SVR-LSM, and (iii) that SVR-LSM is susceptible to misplacement of statistical topographies along the brain's vasculature to a similar extent as mass-univariate analyses.

Mapping human brain lesions and their functional consequences.

Neuroscience has a long history of inferring brain function by examining the relationship between brain injury and subsequent behavioral impairments. The primary advantage of this method over correlative methods is that it can tell us if a certain brain region is necessary for a given cognitive function. In addition, lesion-based analyses provide unique insights into clinical deficits. In the last decade, statistical voxel-based lesion behavior mapping (VLBM) emerged as a powerful method for understanding the architecture of the human brain. This review illustrates how VLBM improves our knowledge of functional brain architecture, as well as how it is inherently limited by its mass-univariate approach. A wide array of recently developed methods appear to supplement traditional VLBM. This paper provides an overview of these new methods, including the use of specialized imaging modalities, the combination of structural imaging with normative connectome data, as well as multivariate analyses of structural imaging data. We see these new methods as complementing rather than replacing traditional VLBM, providing synergistic tools to answer related questions. Finally, we discuss the potential for these methods to become established in cognitive neuroscience and in clinical applications.

Componential Network for the Recognition of Tool-Associated Actions: Evidence from Voxel-based Lesion-Symptom Mapping in Acute Stroke Patients.

The study aimed to elucidate areas involved in recognizing tool-associated actions, and to characterize the relationship between recognition and active performance of tool use.We performed voxel-based lesion-symptom mapping in a prospective cohort of 98 acute left-hemisphere ischemic stroke patients (68 male, age mean ± standard deviation, 65 ± 13 years; examination 4.4 ± 2 days post-stroke). In a video-based test, patients distinguished correct tool-related actions from actions with spatio-temporal (incorrect grip, kinematics, or tool orientation) or conceptual errors (incorrect tool-recipient matching, e.g., spreading jam on toast with a paintbrush). Moreover, spatio-temporal and conceptual errors were determined during actual tool use.Deficient spatio-temporal error discrimination followed lesions within a dorsal network in which the inferior parietal lobule (IPL) and the lateral temporal cortex (sLTC) were specifically relevant for assessing functional hand postures and kinematics, respectively. Conversely, impaired recognition of conceptual errors resulted from damage to ventral stream regions including anterior temporal lobe. Furthermore, LTC and IPL lesions impacted differently on action recognition and active tool use, respectively.In summary, recognition of tool-associated actions relies on a componential network. Our study particularly highlights the dissociable roles of LTC and IPL for the recognition of action kinematics and functional hand postures, respectively.

Verbal and musical short-term memory: Variety of auditory disorders after stroke.

Auditory cognitive deficits after stroke may concern language and/or music processing, resulting in aphasia and/or amusia. The aim of the present study was to assess the potential deficits of auditory short-term memory for verbal and musical material after stroke and their underlying cerebral correlates with a Voxel-based Lesion Symptom Mapping approach (VLSM). Patients with an ischemic stroke in the right (N=10) or left (N=10) middle cerebral artery territory and matched control participants (N=14) were tested with a detailed neuropsychological assessment including global cognitive functions, music perception and language tasks. All participants then performed verbal and musical auditory short-term memory (STM) tasks that were implemented in the same way for both materials. Participants had to indicate whether series of four words or four tones presented in pairs, were the same or different. To detect domain-general STM deficits, they also had to perform a visual STM task. Behavioral results showed that patients had lower performance for the STM tasks in comparison with control participants, regardless of the material (words, tones, visual) and the lesion side. The individual patient data showed a double dissociation between some patients exhibiting verbal deficits without musical deficits or the reverse. Exploratory VLSM analyses suggested that dorsal pathways are involved in verbal (phonetic), musical (melodic), and visual STM, while the ventral auditory pathway is involved in musical STM.

Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis.

The gold standard for identifying stroke lesions is manual tracing, a method that is known to be observer dependent and time consuming, thus impractical for big data studies. We propose LINDA (Lesion Identification with Neighborhood Data Analysis), an automated segmentation algorithm capable of learning the relationship between existing manual segmentations and a single T1-weighted MRI. A dataset of 60 left hemispheric chronic stroke patients is used to build the method and test it with k-fold and leave-one-out procedures. With respect to manual tracings, predicted lesion maps showed a mean dice overlap of 0.696 ± 0.16, Hausdorff distance of 17.9 ± 9.8 mm, and average displacement of 2.54 ± 1.38 mm. The manual and predicted lesion volumes correlated at r = 0.961. An additional dataset of 45 patients was utilized to test LINDA with independent data, achieving high accuracy rates and confirming its cross-institutional applicability. To investigate the cost of moving from manual tracings to automated segmentation, we performed comparative lesion-to-symptom mapping (LSM) on five behavioral scores. Predicted and manual lesions produced similar neuro-cognitive maps, albeit with some discussed discrepancies. Of note, region-wise LSM was more robust to the prediction error than voxel-wise LSM. Our results show that, while several limitations exist, our current results compete with or exceed the state-of-the-art, producing consistent predictions, very low failure rates, and transferable knowledge between labs. This work also establishes a new viewpoint on evaluating automated methods not only with segmentation accuracy but also with brain-behavior relationships. LINDA is made available online with trained models from over 100 patients.

Role of Acute Lesion Topography in Initial Ischemic Stroke Severity and Long-Term Functional Outcomes.

BACKGROUND AND PURPOSE: Acute infarct volume, often proposed as a biomarker for evaluating novel interventions for acute ischemic stroke, correlates only moderately with traditional clinical end points, such as the modified Rankin Scale. We hypothesized that the topography of acute stroke lesions on diffusion-weighted magnetic resonance imaging may provide further information with regard to presenting stroke severity and long-term functional outcomes. METHODS: Data from a prospective stroke repository were limited to acute ischemic stroke subjects with magnetic resonance imaging completed within 48 hours from last known well, admission NIH Stroke Scale (NIHSS), and 3-to-6 months modified Rankin Scale scores. Using voxel-based lesion symptom mapping techniques, including age, sex, and diffusion-weighted magnetic resonance imaging lesion volume as covariates, statistical maps were calculated to determine the significance of lesion location for clinical outcome and admission stroke severity. RESULTS: Four hundred ninety subjects were analyzed. Acute stroke lesions in the left hemisphere were associated with more severe NIHSS at admission and poor modified Rankin Scale at 3 to 6 months. Specifically, injury to white matter (corona radiata, internal and external capsules, superior longitudinal fasciculus, and uncinate fasciculus), postcentral gyrus, putamen, and operculum were implicated in poor modified Rankin Scale. More severe NIHSS involved these regions, as well as the amygdala, caudate, pallidum, inferior frontal gyrus, insula, and precentral gyrus. CONCLUSIONS: Acute lesion topography provides important insights into anatomic correlates of admission stroke severity and poststroke outcomes. Future models that account for infarct location in addition to diffusion-weighted magnetic resonance imaging volume may improve stroke outcome prediction and identify patients likely to benefit from aggressive acute intervention and personalized rehabilitation strategies.

Lesion correlates of patholinguistic profiles in chronic aphasia: comparisons of syndrome-, modality- and symptom-level assessment.

One way to investigate the neuronal underpinnings of language competence is to correlate patholinguistic profiles of aphasic patients to corresponding lesion sites. Constituting the beginnings of aphasiology and neurolinguistics over a century ago, this approach has been revived and refined in the past decade by statistical approaches mapping continuous variables (providing metrics that are not simply categorical) on voxel-wise lesion information (voxel-based lesion-symptom mapping). Here we investigate whether and how voxel-based lesion-symptom mapping allows us to delineate specific lesion patterns for differentially fine-grained clinical classifications. The latter encompass 'classical' syndrome-based approaches (e.g. Broca's aphasia), more symptom-oriented descriptions (e.g. agrammatism) and further refinement to linguistic sub-functions (e.g. lexico-semantic deficits for inanimate versus animate items). From a large database of patients treated for aphasia of different aetiologies (n = 1167) a carefully selected group of 102 first ever ischaemic stroke patients with chronic aphasia (∅ 12 months) were included in a VLSM analysis. Specifically, we investigated how performance in the Aachen Aphasia Test-the standard clinical test battery for chronic aphasia in German-relates to distinct brain lesions. The Aachen Aphasia Test evaluates aphasia on different levels: a non-parametric discriminant procedure yields probabilities for the allocation to one of the four 'standard' syndromes (Broca, Wernicke, global and amnestic aphasia), whereas standardized subtests target linguistic modalities (e.g. repetition), or even more specific symptoms (e.g. phoneme repetition). Because some subtests of the Aachen Aphasia Test (e.g. for the linguistic level of lexico-semantics) rely on rather coarse and heterogeneous test items we complemented the analysis with a number of more detailed clinically used tests in selected mostly mildly affected subgroups of patients. Our results indicate that: (i) Aachen Aphasia Test-based syndrome allocation allows for an unexpectedly concise differentiation between 'Broca's' and 'Wernicke's' aphasia corresponding to non-overlapping anterior and posterior lesion sites; whereas (ii) analyses for modalities and specific symptoms yielded more circumscribed but partially overlapping lesion foci, often cutting across the above syndrome territories; and (iii) especially for lexico-semantic capacities more specialized clinical test-batteries are required to delineate precise lesion patterns at this linguistic level. In sum this is the first report on a successful lesion-delineation of syndrome-based aphasia classification highlighting the relevance of vascular distribution for the syndrome level while confirming and extending a number of more linguistically motivated differentiations, based on clinically used tests. We consider such a comprehensive view reaching from the syndrome to a fine-grained symptom-oriented assessment mandatory to converge neurolinguistic, patholinguistic and clinical-therapeutic knowledge on language-competence and impairment.

Neuroimaging determinants of cognitive impairment in the memory clinic: how important is the vascular burden?

While neurodegenerative and vascular neurocognitive disorder (NCD) often co-occur, the contribution of vascular lesions, especially stroke lesions identified on MRI, to global cognition in a real-life memory clinic population remains unclear. The main objective of this retrospective study was to determine NCD neuroimaging correlates: the GM atrophy pattern and vascular lesions (especially stroke lesion localization by voxel-based lesion-symptom mapping, VLSM) in a memory clinic. We included 336 patients with mild or major NCD who underwent cerebral MRI and a neuropsychological assessment. The GM atrophy pattern (obtained by voxel-based morphometry, VBM) and the stroke lesion localization (obtained by VLSM) associated with G5 z-score (a global cognitive score), were included as independent variables with other neuroimaging and clinical indices in a stepwise linear regression model. The mean age was 70.3 years and the mean MMSE score 21.3. On MRI, 75 patients had at least one stroke lesion. The G 5 z-score was associated with GM density in the pattern selected by the VBM analysis (R2 variation = 0.166, p < 0.001) and the presence of a stroke lesion in the region selected by the VSLM analysis (mainly in the right frontal region; R2 variation = 0.018, p = 0.008). The interaction between the two factors was insignificant (p = 0.374). In conclusion, in this first study combining VBM and VLSM analysis in a memory clinic, global cognition was associated with a specific GM atrophy pattern and the presence of a stroke lesion mainly in the right frontal region.

Dexterity in the Acute Phase of Stroke: Impairments and Neural Substrates.

BACKGROUND: Stroke can impair manual dexterity, leading to loss of independence following incomplete recovery. Enhancing our understanding of dexterity impairment may improve neurorehabilitation. OBJECTIVES: The study aimed to measure dexterity components in acute stroke patients with and without hand motor deficits, compare them to those of healthy controls (HC), and to explore the neural substrates involved in specific components of dexterity. METHODS: We used the Dextrain Manipulandum to quantify fine finger force control, finger selection accuracy, coactivation, and reaction time (RT). Dexterity was evaluated twice (2 days apart) in 74 patients and 14 HC. Voxel-Lesion-Symptom-Mapping (VLSM) was used to analyze the relationship between tissue damage and dexterity. Results. Due to severe paresis or fatigue, 24 patients could not perform these tasks. In 50 patients (included 4.6 ± 3.3 days post-stroke), finger force control improved (P < .001), as it did in HC (P = .03) who performed better than patients on both evaluations. Accuracy of finger selection did not improve significantly in any group, but the HC performed better on both evaluations. Unexpectedly, coactivation was better in patients than in HC at D3 (P = .03). There were no between-group differences in RT. VLSM showed that damage to the superior temporal gyrus (STG) impaired finger force control while damage to the posterior limb of the internal capsule (PLIC) impaired finger selectivity. CONCLUSIONS: Acute stroke affecting the STG or PLIC impaired selective components of dexterity. Patients with mild to moderate impairment showed better finger force control and accuracy selection within 48 hours, suggesting the feasibility of detecting early dexterity improvements.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches.

Background: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values. Materials and methods: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit). Results: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration. Conclusions: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.